Science: Computing power for Research
Simulations, data analysis and research data management – with GPU and CPU power booked per project instead of spending years in a funding application. Flexible, cost-efficient and always up to date.
- Maximum computing capacity – GPU power without your own infrastructure.
- Latest GPU technology – dedicated NVIDIA cards, regularly refreshed.
- Flexibly scalable – increase or reduce performance as needed.
- Transparent costs – no hidden costs, billed by the hour, with no minimum contract term.
Infrastructure for projects rather than applications
Research is project-based – computing power should be too.
GPU for computations
Simulations and machine learning analyses run on dedicated GPUs from €92.59 per month (billed by the hour) – booked for the duration of the project and charged to third-party funding.
FAIR research data
S3 Object Storage makes datasets available in versioned and citable formats – from raw data to publication supplements.
Clusters as required
Scaling batch jobs in Kubernetes – large parameter studies result in a large number of nodes, after which the cluster shrinks to zero.
Data Protection for Studies
Personal research data remains in German, C5-attested data centres – compliant with ethics committee requirements.
Accelerated workloads with NVIDIA GPUs in the cloud
Especially in data-intensive disciplines such as genomics, climate research or materials science, conventional systems are quickly overwhelmed – cloud GPUs process these workloads at scale.
Training
For faster processing and optimisation of large datasets – train models on dedicated GPUs without waiting for shared clusters.
Fine-tuning
For higher accuracy through targeted optimisation – existing models are adapted to your research question, billed by the hour only for the computing time.
Inference
For the efficient use of trained models for more precise analyses – from screening large datasets to evaluating ongoing measurement series.
Simulation, rendering & big data
Compute-intensive workloads such as deep learning, 3D rendering, scientific simulations and big data analyses run efficiently – with maximum flexibility and without high investment costs.
Reproducible research on reproducible infrastructure
Analyses are carried out in Jupyter on a VM, scaled as container jobs in Kubernetes, and the results – along with the environment – are archived in S3; every publication remains traceable. Project-specific billing provides quotas and cReports for the management of third-party funding.
- Interactive – Jupyter & RStudio on VMs
- Batch processing – Kubernetes for parametric studies
- Data Archive – S3 versioned and citable
- Suitable for third-party funding – Costs per project
The right centron products
Customers typically implement this use case using these building blocks – which can be combined and expanded at any time.
- RTX A4000 from €92.59 per month
- Dedicated, not shared
- No minimum term
- AutoScaler included
- Traffic at a fixed price
- CI/CD-ready
- S3-compatible API
- Free traffic
- Unlimited scalability
Which cloud infrastructure is best suited to the scientific community?
Cloud for science: computing clusters, GPU nodes and storage for research projects – flexible, eligible for funding, based in Germany. The core component is Cloud GPU, starting from €92.59 per month – billed by the hour, with no minimum contract term. This is supplemented, as required, by Kubernetes and S3 Object Storage. The service is hosted in compliance with the GDPR in centron’s own certified to ISO 27001 on the basis of IT-Grundschutz data centres, which hold a BSI C5:2020 Type 1 attestation. New accounts receive a €200 starting credit.
| Building block | Price |
|---|---|
| Cloud GPU | from €92.59 per month |
| Kubernetes | from €29.99 per month |
| S3 Object Storage | from €5.00 per month |
Frequently asked questions
Why are cloud GPUs so useful for scientific research?
How can a cloud GPU be described?
What advantages do cloud GPUs offer over local GPU servers?
Does centron also offer servers for research projects?
Is this cost-recovery model suitable for externally funded projects?
How much does it cost to get started?
Can this be implemented in a way that complies with the GDPR?
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